@inproceedings{StaatDuong2016,
  author    = {Staat, Manfred and Duong, Minh Tuan},
  title     = {Smoothed Finite Element Methods for Nonlinear Solid Mechanics Problems: 2D and 3D Case Studies},
  series = {Proceedings of the National Science and Technology Conference on Mechanical - Transportation Engineering (NSCMET 2016), 13th October 2016, Hanoi, Vietnam, Vol.2},
  booktitle = {Proceedings of the National Science and Technology Conference on Mechanical - Transportation Engineering (NSCMET 2016), 13th October 2016, Hanoi, Vietnam, Vol.2},
  pages     = {440 -- 445},
  year      = {2016},
  abstract  = {The Smoothed Finite Element Method (SFEM) is presented as an edge-based and a facebased techniques for 2D and 3D boundary value problems, respectively. SFEMs avoid shortcomings of the standard Finite Element Method (FEM) with lower order elements such as overly stiff behavior, poor stress solution, and locking effects. Based on the idea of averaging spatially the standard strain field of the FEM over so-called smoothing domains SFEM calculates the stiffness matrix for the same number of degrees of freedom (DOFs) as those of the FEM. However, the SFEMs significantly improve accuracy and convergence even for distorted meshes and/or nearly incompressible materials. Numerical results of the SFEMs for a cardiac tissue membrane (thin plate inflation) and an artery (tension of 3D tube) show clearly their advantageous properties in improving accuracy particularly for the distorted meshes and avoiding shear locking effects.},
  language  = {en}
}
@inproceedings{TranTranMatthiesetal.2016,
  author    = {Tran, Ngoc Trinh and Tran, Thanh Ngoc and Matthies, Hermann G. and Stavroulakis, Georgios Eleftherios and Staat, Manfred},
  title     = {FEM Shakedown of uncertain structures by chance constrained programming},
  series = {PAMM Proceedings in Applied Mathematics and Mechanics},
  volume    = {16},
  booktitle = {PAMM Proceedings in Applied Mathematics and Mechanics},
  number    = {1},
  issn      = {1617-7061},
  doi       = {10.1002/pamm.201610346},
  pages     = {715 -- 716},
  year      = {2016},
  language  = {en}
}
@article{LeversStaatLaack2016,
  author    = {Levers, A. and Staat, Manfred and Laack, Walter van},
  title     = {Analysis of the long-term effect of the MBST® nuclear magnetic resonance therapy on gonarthrosis},
  series = {Orthopedic Practice},
  volume    = {47},
  journal   = {Orthopedic Practice},
  number    = {11},
  pages     = {521 -- 528},
  year      = {2016},
  language  = {en}
}
@article{JahnkeRousselHombachetal.2016,
  author    = {Jahnke, Siegfried and Roussel, Johanna and Hombach, Thomas and Kochs, Johannes and Fischbach, Andreas and Huber, Gregor and Scharr, Hanno},
  title     = {phenoSeeder - A robot system for automated handling and phenotyping of individual seeds},
  series = {Plant physiology},
  volume    = {172},
  journal   = {Plant physiology},
  number    = {3},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {0032-0889},
  doi       = {10.1104/pp.16.01122},
  pages     = {1358 -- 1370},
  year      = {2016},
  abstract  = {The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.},
  language  = {en}
}
@misc{Schreiber2016,
  author    = {Schreiber, Marc},
  title     = {Mit Maximum-Entropie das Parsing nat{\"u}rlicher Sprache erlernen},
  publisher = {FH Aachen},
  address   = {Aachen},
  pages     = {23 Seiten},
  year      = {2016},
  abstract  = {F{\"u}r die Verarbeitung von nat{\"u}rlicher Sprache ist ein wichtiger Zwischenschritt das Parsing, bei dem f{\"u}r S{\"a}tze der nat{\"u}rlichen Sprache Ableitungsb{\"a}ume bestimmt werden. Dieses Verfahren ist vergleichbar zum Parsen formaler Sprachen, wie z. B. das Parsen eines Quelltextes. Die Parsing-Methoden der formalen Sprachen, z. B. Bottom-up-Parser, k{\"o}nnen nicht auf das Parsen der nat{\"u}rlichen Sprache {\"u}bertragen werden, da keine Formalisierung der nat{\"u}rlichen Sprachen existiert [3, 12, 23, 30]. In den ersten Programmen, die nat{\"u}rliche Sprache verarbeiten [32, 41], wurde versucht die nat{\"u}rliche Sprache mit festen Regelmengen zu verarbeiten. Dieser Ansatz stieß jedoch schnell an seine Grenzen, da die Regelmenge nicht vollst{\"a}ndig sowie nicht minimal ist und wegen der ben{\"o}tigten Menge an Regeln schwer zu verwalten ist. Die Korpuslinguistik [22] bot die M{\"o}glichkeit, die Regelmenge durch Supervised-Machine-Learning-Verfahren [2] abzul{\"o}sen. Teil der Korpuslinguistik ist es, große Textkorpora zu erstellen und diese mit sprachlichen Strukturen zu annotieren. Zu diesen Strukturen geh{\"o}ren sowohl die Wortarten als auch die Ableitungsb{\"a}ume der S{\"a}tze. Vorteil dieser Methodik ist es, dass repr{\"a}sentative Daten zur Verf{\"u}gung stehen. Diese Daten werden genutzt, um mit Supervised-Machine-Learning-Verfahren die Gesetzm{\"a}ßigkeiten der nat{\"u}rliche Sprachen zu erlernen. Das Maximum-Entropie-Verfahren ist ein Supervised-Machine-Learning-Verfahren, das genutzt wird, um nat{\"u}rliche Sprache zu erlernen. Ratnaparkhi [25] nutzt Maximum-Entropie, um Ableitungsb{\"a}ume f{\"u}r S{\"a}tze der nat{\"u}rlichen Sprache zu erlernen. Dieses Verfahren macht es m{\"o}glich, die nat{\"u}rliche Sprache (abgebildet als Σ∗) trotz einer fehlenden formalen Grammatik zu parsen.},
  language  = {de}
}
@inproceedings{BaeckerKochGeigeretal.2016,
  author    = {B{\"a}cker, Matthias and Koch, C. and Geiger, F. and Eber, F. and Gliemann, H. and Poghossian, Arshak and Sch{\"o}ning, Michael Josef},
  title     = {A New Class of Biosensors Based on Tobacco Mosaic Virus and Coat Proteins as Enzyme Nanocarrier},
  series = {Procedia Engineering},
  volume    = {Vol. 168},
  booktitle = {Procedia Engineering},
  issn      = {1877-7058},
  doi       = {10.1016/j.proeng.2016.11.228},
  pages     = {618 -- 621},
  year      = {2016},
  language  = {en}
}
@inproceedings{PoghossianBronderSchejaetal.2016,
  author    = {Poghossian, Arshak and Bronder, Thomas and Scheja, S. and Wu, Chunsheng and Metzger-Boddien, C. and Keusgen, M. and Sch{\"o}ning, Michael Josef},
  title     = {Label-free Electrostatic Detection of DNA Amplification by PCR Using Capacitive Field-effect Devices},
  series = {Procedia Engineering},
  volume    = {Vol. 168},
  booktitle = {Procedia Engineering},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1877-7058},
  doi       = {10.1016/j.proeng.2016.11.512},
  pages     = {514 -- 517},
  year      = {2016},
  abstract  = {A capacitive field-effect EIS (electrolyte-insulator-semiconductor) sensor modified with a positively charged weak polyelectrolyte of poly(allylamine hydrochloride) (PAH)/single-stranded probe DNA (ssDNA) bilayer has been used for a label-free electrostatic detection of pathogen-specific DNA amplification via polymerase chain reaction (PCR). The sensor is able to distinguish between positive and negative PCR solutions, to detect the existence of target DNA amplicons in PCR samples and thus, can be used as tool for a quick verification of DNA amplification and the successful PCR process.},
  language  = {en}
}
@inproceedings{DroszezSannoGoldmannetal.2016,
  author    = {Droszez, Anna and Sanno, Maximilian and Goldmann, Jan-Peter and Albracht, Kirsten and Br{\"u}ggemann, Gerd-Peter and Braunstein, Bjoern},
  title     = {Differences between take-off behavior during vertical jumps and two artistic elements},
  series = {34th International Conference of Biomechanics in Sport, Tsukuba, Japan, July 18-22, 2016},
  booktitle = {34th International Conference of Biomechanics in Sport, Tsukuba, Japan, July 18-22, 2016},
  issn      = {1999-4168},
  pages     = {577 -- 580},
  year      = {2016},
  language  = {en}
}
@article{KolditzAlbinBrueggemannetal.2016,
  author    = {Kolditz, Melanie and Albin, Thivaharan and Br{\"u}ggemann, Gert-Peter and Abel, Dirk and Albracht, Kirsten},
  title     = {Robotergest{\"u}tztes System f{\"u}r ein verbessertes neuromuskul{\"a}res Aufbautraining der Beinstrecker},
  series = {at - Automatisierungstechnik},
  volume    = {64},
  journal   = {at - Automatisierungstechnik},
  number    = {11},
  publisher = {De Gruyter},
  address   = {Berlin},
  issn      = {2196-677X},
  doi       = {10.1515/auto-2016-0044},
  pages     = {905 -- 914},
  year      = {2016},
  abstract  = {Neuromuskul{\"a}res Aufbautraining der Beinstrecker ist ein wichtiger Bestandteil in der Rehabilitation und Pr{\"a}vention von Muskel-Skelett-Erkrankungen. Effektives Training erfordert hohe Muskelkr{\"a}fte, die gleichzeitig hohe Belastungen von bereits gesch{\"a}digten Strukturen bedeuten. Um trainingsinduzierte Sch{\"a}digungen zu vermeiden, m{\"u}ssen diese Kr{\"a}fte kontrolliert werden. Mit heutigen Trainingsger{\"a}ten k{\"o}nnen diese Ziele allerdings nicht erreicht werden. F{\"u}r ein sicheres und effektives Training sollen durch den Einsatz der Robotik, Sensorik, eines Regelkreises sowie Muskel-Skelett-Modellen Belastungen am Zielgewebe direkt berechnet und kontrolliert werden. Auf Basis zweier Vorstudien zu m{\"o}glichen Stellgr{\"o}ßen wird der Aufbau eines robotischen Systems vorgestellt, das sowohl f{\"u}r Forschungszwecke als auch zur Entwicklung neuartiger Trainingsger{\"a}te verwendet werden kann.},
  language  = {de}
}
@incollection{Bialonski2016,
  author    = {Bialonski, Stephan},
  title     = {Are interaction clusters in epileptic networks predictive of seizures?},
  series = {Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics},
  booktitle = {Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics},
  publisher = {CRC Press},
  isbn      = {978-143983886-0},
  pages     = {349 -- 355},
  year      = {2016},
  language  = {en}
}